Is speed still the best CX metric? In the era of AI, Customer Assurance and brand trust are the top differentiators. Discover why customer confidence beats quick resolution in the new CX Insight Magazine: 📖 #CustomerExperience# #AITrust# #CXLeadership#

It's exactly three months since I launched Ground Level AI! What a ride it's already been: On June 16, I said Ground Level AI would be focused on one big question: What happens when AI meets the real world? AI is an overwhelming beat, but what matters most is still underreported amid the hype cycle. The massive infrastructure buildout, the reality for enterprise companies, the security stakes, the policy decisions, the societal consequences, the geopolitical fault lines: these are the stories that will define how this technology actually lands.  That's all happening right now, swirling amid today's headlines, and it's been exciting (and humbling) to report on it all. Over the past three months, I've covered everything from AI data centers and AI-orchestrated ransomware attacks to the AI talent wars for forward-deployed engineers; the AI coding agent hangover; AI's culture wars and the AI safety vs. cybersecurity collision. I've traveled to San Francisco to listen to experts discuss open source AI and the latest in enterprise adoption; to Las Vegas to be the first to report on the OpenAI-Hugging Face briefing at Black Hat; to Iowa to ride an AI-powered tractor; and I'm headed to Pittsburgh and then Amsterdam this week to moderate discussions about AI trust; AI and creativity; and real-world intelligence. I'm tantalizingly close to 5,000 Ground Level AI subscribers now, and the momentum seems to be there for a publication and podcast that looks beyond the model launches, product news, funding rounds, and predictions about the future. I'm most interested in digging into the messy stuff: The infrastructure AI requires, the realities of enterprise deployment, the security and governance challenges, the policy and geopolitical battles, and the workers and communities and industries being remade by it. That's where I intend to keep reporting. Want to follow where I'm headed? Meet me at

Looking forward to a very insightful day on Oct 1 at Stanford on one of the most critical issues in tech— AI Trust & Safety and 3rd party independent attestation @DarioAmodei @sama @elonmusk @satyanadella @sundarpichai

This was the week the control layer became the story. Agentic finance is no longer waiting for AI agents to become capable enough to transact. Payment rails are going live, banks are assigning agents real system access, and regulators are beginning to define who is responsible when autonomous software moves money. Here are seven developments that mattered: 1. The @x402 Foundation became fully operational under the Linux Foundation, completing Coinbase’s contribution of the protocol to open governance. This is more than ecosystem growth. Once a payment protocol is governed beyond a single vendor, it has a much stronger path toward becoming shared infrastructure for the agentic web. 2. @hmtreasury published its Financial Services AI Adoption Plan, identifying agentic payments as a near-term test case for broader autonomous finance. Its highest-priority recommendation calls for an agentic-payment trust framework built around three pillars: • clear legal liability • standardized Know Your Agent protocols • interoperable authentication and governance This is not regulation yet. But the conversation has moved from abstract AI risk to a much more practical question: when an agent transacts, who authorized it, what was it allowed to do, and who is accountable? 3. A joint @Visa and @artemis report divided agentic commerce into two categories: Macro-commerce: agents purchasing on behalf of people, where cards remain a natural fit. Micro-commerce: software paying software for APIs, data or compute, often in amounts too small for traditional card economics. Using adjusted onchain data through April 21, the report found that x402 had processed roughly 109.6 million transactions and $15 million in volume. Visa’s conclusion was not cards versus stablecoins, but a future in which both rails coexist. Two days later, Visa launched its Stablecoin Platform in beta, combining wallet infrastructure, minting and redemption with dual approvals, audit logs, passkeys and transfer allowlists. 4. A @KPMG survey cited by @Reuters found that 51% of banks are already piloting AI agents. @BNYglobal treats some agents as “digital employees,” giving them login credentials, assigned tasks and human managers. @UBS agents can prepare and execute trades or transfers after an adviser makes the decision. @MorganStanley is testing client-facing assistants while keeping portfolio decisions under human oversight. The emerging operating model is not simply human or machine. It is delegated access paired with named accountability. 5. @Entrust_Corp launched an Agentic AI Trust Accelerator focused on four production requirements: verifiable identity, real-time authorization, cryptographic assurance and proof of action. The program reflects a wider shift across enterprise AI. An agent cannot be trusted simply because its model is capable. Its identity, delegated authority and actions need to remain verifiable across systems and organizations. 6. @OpenAI introduced GPT-Red, an automated red-teaming model designed to find prompt-injection vulnerabilities. In one controlled exercise, GPT-Red compromised a live autonomous vending-machine agent, changed the price of expensive products to $0.50 and cancelled another customer’s order. Model-level defenses are improving. But once agents can access systems and move value, safety cannot depend entirely on the model correctly interpreting every instruction. External policies, execution controls and auditability still matter. 7. @Kimi_Moonshot released Kimi K3, a 2.8-trillion-parameter model built for long-horizon coding, knowledge work and tool use, with a one-million-token context window. One of its own disclosed limitations is “excessive proactiveness”: on ambiguous tasks, the model may make unexpected decisions on the user’s behalf. That may be the clearest description of the next infrastructure problem. Agents are becoming better at acting for longer periods with less supervision. The systems constraining those actions now need to advance just as quickly. The pattern across the week is clear: Models are getting better at deciding. Payment rails are getting better at settling. The open question is who controls the moment between the two. At Vishwa, that is the layer we are building for: turning an agent’s intent into an authorized, policy-bound and verifiable financial action-before money moves.

Whenever you are told “Trust the experts” you are better off doing the opposite. Remember when Covid “Trust the experts” led to a civilizational hoax that broke our economy and retarded our children? The AI “Trust the experts” version will make that pale in comparison. I’d encourage all of you to approach this with wonder but also a healthy dose of skepticism.